Identifying and expanding implicitly temporally qualified queries

ABSTRACT

Methods and apparatus are described for identifying implicitly temporally qualified queries, i.e., queries for which a time period is implied but not explicitly stated, and for expanding such queries to include one or more temporal references.

BACKGROUND OF THE INVENTION

The present invention relates to techniques for expanding search queriesand, more specifically, to identifying and expanding queries which areimplicitly temporally qualified.

Search engines employ a variety of techniques in attempting to providethe most relevant search results in response to user search queries. Atleast some of these techniques attempt to make up for the fact that mostsearch queries may be characterized by some level of inherent ambiguity.That is, when users formulate queries they often omit search termswhich, if included, would yield search results more closely correlatedwith their actual intent. To deal with this, search engines often useinformation relating to the user (e.g., demographic and/or personalinformation, expressed preferences, geographic information, past onlinebehavior, etc.) to order search results based on the assumption thatthis kind of information generally correlates to the intent implicit ina particular user's search queries.

However, while such techniques have been shown to have some generalutility, they are not necessarily reflective of a particular user'simplicit intent in specific instances, thus leading to rendering ofsearch results not reflective of the user's intent and the possibilityof negative user experience.

SUMMARY OF THE INVENTION

According to one class of embodiments of the present invention, methodsand apparatus are provided for responding to a first search query thatdoes not include an explicit temporal reference. The first search queryis received from a user device and determined to be implicitlytemporally qualified with reference to a first query term included inthe first search query and query log data that relate a plurality ofprevious query terms included in previous search queries to specifictemporal references included in the previous search queries. The firstsearch query is expanded to include one or more of the specific temporalreferences. First search results are generated using the expanded firstsearch query, and transmitted to the user device.

According to another class of embodiments, methods and apparatus areprovide for presenting search results on a user device in response to afirst search query that does not include an explicit temporal reference.A search interface is provided on the user device in which a user entersthe first search query. A search results interface is provided on a userdevice in which search results are presented that are responsive to afirst expanded version of the first search query. The first expandedversion of the first search query includes one or more temporalreference not included in the first search query.

According to yet another class of embodiments, methods and apparatus areprovided for responding to a first search query that does not include anexplicit temporal reference. The first search query is received from auser device and determined to be implicitly temporally qualified withreference to a first query term included in the first search query andquery log data that relate a plurality of previous query terms includedin previous search queries to specific temporal references included inthe previous search queries. First search results responsive to thefirst search query are generated, and transmitted to the user device.The first search results are ranked in accordance with a predeterminedranking function derived with reference to the query log data.

According to yet another class of embodiments, a system is provided forresponding to a first search query that does not include an explicittemporal reference. At least one data store has query log data storedtherein. The query log data relates a plurality of previous query termsincluded in previous search queries to specific temporal referencesincluded in the previous search queries. A least one computing device isconfigured to receive the first search query from a user device anddetermine that the first search query is implicitly temporally qualifiedwith reference to a first query term included in the first search queryand the query log data. The at least one computing device being furtherconfigured to generate first search results responsive to the firstsearch query and taking the query log data into account, and to transmitthe first search results to the user device.

A further understanding of the nature and advantages of the presentinvention may be realized by reference to the remaining portions of thespecification and the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart illustrating generation of query log data for usein identifying implicitly temporally qualified queries according to aparticular embodiment of the invention.

FIG. 2 is a flowchart illustrating identification and reformulation ofimplicitly temporally qualified queries according to a particularembodiment of the invention.

FIG. 3 is a flowchart illustrating filtering of query log data for usein identifying implicitly temporally qualified queries according to aparticular embodiment of the invention.

FIG. 4 is a simplified diagram of a computing environment in whichembodiments of the invention may be implemented.

FIG. 5 is a flowchart illustrating identification of implicitlytemporally qualified queries and generating search results according toa particular embodiment of the invention.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

Reference will now be made in detail to specific embodiments of theinvention including the best modes contemplated by the inventors forcarrying out the invention. Examples of these specific embodiments areillustrated in the accompanying drawings. While the invention isdescribed in conjunction with these specific embodiments, it will beunderstood that it is not intended to limit the invention to thedescribed embodiments. On the contrary, it is intended to coveralternatives, modifications, and equivalents as may be included withinthe spirit and scope of the invention as defined by the appended claims.In the following description, specific details are set forth in order toprovide a thorough understanding of the present invention. The presentinvention may be practiced without some or all of these specificdetails. In addition, well known features may not have been described indetail to avoid unnecessarily obscuring the invention.

According to various embodiments of the present invention, techniquesare provided by which search queries which do not explicitly includetemporal references are identified as likely to be implicitly temporallyqualified. Such queries may then be reformulated to include one or moretemporal references, and search results may be generated based on thereformulated query.

Examples of specific embodiments of the invention that are based onquery log analysis are described below. These embodiments focus on theimplicit temporal qualifications being years. However, it will beunderstood that the techniques described herein may be readily appliedto a much broader range of temporal references. Referring now to theflowchart of FIG. 1, a query log (e.g., from Yahoo! Search, Google, orany other search engine or service) is analyzed to count the occurrencesof each query which has a year explicitly specified (102), e.g., as aprefix or suffix. So, for example, for the query “Olympics,” eachoccurrence of the term “Olympics” with a particular year, e.g., “2000,”“2004,” “2008,” etc., is counted. That is, an “explicitly yearqualified” count is performed for each combination of the query term anda particular year.

Counts are also determined for the total number of occurrences of eachquery term (104). The ratio of the explicitly year qualified count foreach query term/year combination to the total count for that query termis then determined (106). According to a particular embodiment, querylog data for a given query term/year combination is only stored for usein identify implicitly year qualified queries (110) if the ratio forthat query term and year exceeds some programmable threshold value(108), e.g., in the range of about 0.10-0.20. That is, only if thethreshold is exceeded will queries containing that query term be deemedlikely to be implicitly year qualified. As will be understood, theappropriate threshold for a given application may be empiricallydetermined to avoid misidentification of spurious queries.

It will be understood that the ratio described above is merely anexample of one measure which might be used to identify particular queryterms for use in identifying implicitly year qualified queries. Forexample, a ratio of the explicitly year qualified count to some othernumber (e.g., a count of the number of occurrences of the query termwithout an explicit reference to a year) might be used. More generally,any measure which indicates that a significant portion of the queriescontaining a particular query term were year qualified may be used.

According to a particular implementation, processing of the query logsis done offline and the results cached for use during online searchquery processing. As discussed above, these cached data may be updatedfrom time to time to be reflective of trends in search query patterns.And it will be understood that the manner in which this information isstored may vary considerably without departing from the invention.According to a particular implementation, the cached query log data arerepresented in a table in which each query term has one or moreassociated year entries, each of which has its associated ratio value.

It should also be noted that a given query term will typically beassociated in the query log data with multiple years, i.e., the queryterm may have more than one year for which the ratio (or otherappropriate measure) exceeds the threshold (or otherwise indicates thatqueries containing the query term are likely to be implicitly yearqualified). In this way, for any given query term a distribution overyears is maintained. And as will be understood, these query log data maybe updated on an ongoing basis (112) so that they reflect changes insearch behaviors.

Once query log data have been generated they may be used to identify andreformulate subsequent queries including the query terms represented inthe query log data. An example of the operation of one such embodimentsis illustrated in the flowchart of FIG. 2. When a query is received(202), it is determined whether the query contains a temporal reference,e.g., a reference to a year (204), and if not, whether the querycontains a query term represented in the query log data (206). If aquery term is present which is also in the query log data, the query isconsidered an implicitly year qualified query, and the query istherefore reformulated to include one or more temporal references, e.g.,years, (208). Search results are then generated that are responsive tothe reformulated query (210).

As discussed above, a particular query term may be associated withmultiple years in the query log data. In such cases, a variety ofdifferent decisions can be made regarding whether and how to modify animplicitly year qualified query containing that query term. For example,the query can be modified to include each of the years. In such cases,the added query terms and/or the search results can be weighted toreflect different emphases for the different years, e.g., in accordancewith the ratios corresponding to each.

Alternatively, a decision could be made to modify the query only toinclude the year having the highest ratio or the most recent year. Insuch cases, there is a possibility that this will result in the wrongyear being selected. However, this risk may be mitigated by providingadditional feedback to the user such as, for example, a questionassociated with the search results, e.g., “Did you mean “2004 Olympics?”The user could then select a link associated with this question toobtain alternative search results for a different year or even for theoriginal query formulation. Another alternative would be to present somesort of representation of the distribution over years for the queryterm, e.g., a timeline, from which the user could select a relevantyear.

More generally, embodiments are contemplated in which the user isinformed in some way that the original query was modified to includewhat was believed to be an implicit year (212) and then is provided withsome mechanism to reject the modification and/or select a differentmodification, i.e., a different year (214). A variety of otheralternatives for handling queries with multiple year possibilities willbe apparent to those of skill in the art.

According to some embodiments, the query log data that are used toidentify implicitly year qualified queries are derived from queries thatwere reformulated by users within sessions. An example of this approachis illustrated in the flowchart of FIG. 3. The query log is analyzed toidentify each time a user reformulates a query including a particularquery term to add a temporal reference, e.g., a year, within a givensession (302). If queries including a particular query term arereformulated multiple times using different years (304) they are kept inthe data set (306). For example, if the query term “miss universe” wasreformulated using every year from 2001 through 2008, then new queriescontaining that query term without an explicit reference to a year areconsidered likely to be implicitly year qualified queries. Theappropriate threshold number of different years for inclusion in thedata set may be identified empirically and may vary considerably withoutdeparting from the scope of the invention. According to a particularimplementation, reformulated queries are filtered using a log-likelihoodratio test (see, for example, C. Manning and H. Schutze. Foundation ofstatistical natural language processing, MIT Press, 1999). Thiseliminates most coincidental occurrences of a query and a reformulation,i.e., only reformulations which frequently occur with a given year areincluded, and any infrequent rewriting of a query to add a year will notbe included.

These data may then be further processed for inclusion in a cache table(308) for use in identifying implicitly temporally qualified queries.According to some embodiments, this further processing may be done in amanner similar to that described above with reference to FIG. 1. Thatis, the raw query logs may first be filtered to include only datarelating to reformulated queries. These data may then be processed toidentify the query terms and frequencies. And as with the frequencyapproach, this approach may involve ongoing offline mining of query logs(310) (e.g., Yahoo! Search query logs) to build a cache table which isthen used to process search queries in real time.

A more formal description of certain aspects of a specific embodiment ofthe invention will now be provided. One important measure of the queriesmined from the query log is how strongly they are associated with agiven year. This value, which we call the year qualified weight, isdefined as:

w(q,y)=#(q,y)+#(y,q)   (1)

where # (q,y) denotes the number of times that the base query q ispost-qualified with the year y in either the reformulated query data(e.g., see FIG. 2) or in the raw query log (e.g., see FIG. 1).Similarly, # (y,q) is the number of times that q is pre-qualified withthe year y. Therefore, this weight measures how likely q is to bequalified with y, which forms the basis of the mining and analysis inthis particular embodiment.

The following methodology may then be employed for automatically miningimplicitly year qualified queries. Given a query q, we first computew(q, y) for all plausible years. We then use the following function todetermine if a query is implicitly year qualified:

${{isIYQQ}(q)} = \{ \begin{matrix}1 & {{\{ {y:{{w( {q,y} )} > 0}} \} } \geq 2} \\0 & {otherwise}\end{matrix} $

which simply states that a query is considered likely to be implicitlyyear qualified if it is qualified by at least two unique years. In thisexample, we set the threshold at 2 because we are interested intemporally recurring events. However, it is possible to mine one-timeevents by lowering the threshold to 1, although this may also introducespurious or noisy detections.

Even though a query is identified as implicitly year qualified does notnecessarily mean that the query should always be treated as temporal innature. Consider the query “chi”, which corresponds to the name of apopular human-computer interaction conference. This query is clearlytemporal, and indeed, the algorithms described herein would detect thequery as being implicitly year qualified. However, “chi” is a verycommon term that is often reformulated or qualified in many differentways, including “chi squared” (statistical test), “chi chis”(restaurant), and “chi omega” (sorority). In fact, these otherreformulations and qualifications are much more common than the temporalones. We call this phenomenon temporal ambiguity. Implicitly yearqualified queries, such as “chi” that are associated with many differentreformulations are temporally ambiguous, whereas other queries, such as“cikm” (an abbreviation of an annual conference) that are almostexclusively reformulated or qualified with a year are deemed temporallyunambiguous. According to a particular embodiment, we quantify temporalambiguity as follows:

${\alpha (q)} = \frac{\sum\limits_{y}{w( {q,y} )}}{{\sum\limits_{x}{\# ( {q.x} )}} + {\sum\limits_{x|}{\# ( {x.q} )}}}$

where the sums Σx#(q,x) and Σx(x,q) go over all pre- andpost-qualifications for the query q. It should be apparent that if thequery is always qualified with a year, i.e., x=y, then α(q)=1. Althoughwe call this measure temporal ambiguity, it may also be interpreted as aconfidence value that the query is temporal.

Now that we have shown that we can reliably mine implicitly yearqualified queries, we now describe how the information mined can be usedto improve search quality for these types of queries using result setreordering and temporal query expansion techniques. According to someimplementations, such techniques attempt to bias the result sets towardsdocuments that contain highly weighted years that are associated withthe query while taking temporal ambiguity and freshness into account.

According to one approach, the score of a document d may be adjusted inresponse to query q according to the years contained in the document.That is, given an implicitly year qualified query q, the qualified yearsassociated with q may be weighted as follows:

${z( {q,y} )} = {{N( {{y;\mu},\sigma^{2}} )} \cdot {\alpha (q)} \cdot \frac{w( {q,y} )}{\max_{y}{w( {q,y} )}}}$

where N(y; μ, σ²) is a normal distribution with mean μ and variance σ²,α(q) is the temporal ambiguity (equation (2)), and w(q, y) is the numberof times that query q is qualified with year y (equation (1)).

The first component, N(y; μ, σ²), models the a priori preference for agiven year. According to one embodiment, a normal distribution was usedwith μ=2008 and σ²=1 in order to impose a preference for recent years.However, the normal distribution can be replaced with any reasonabledistribution or weighting to impose other reasonable types ofpreferences. Indeed, this value can be set to a constant, which wouldcorrespond to a uniform preference over years. The second component,α(q), models the confidence that the given query is temporal. If α(q) islarge, then the query is very likely to be temporal, and therefore willreceive a higher weight. The third component, [w(q,y)]/[max_(y)w(q,y)],is the normalized year qualified weight for the year as estimated fromthe query log. Thus, recent years that have large year qualified weightsfor temporally unambiguous queries will have large z(q, y) weights.

Given a ranking function S(q, d) that produces a score for documents din response to query q, the z(q, y) weights may be used to temporallybias S(q, d) as follows:

${S^{\prime}( {q,d} )} = {{S( {q,d} )} + {\lambda {\sum\limits_{y \in d}{z( {q,y} )}}}}$

where S′(q, d) is the temporally biased score, S(q, d) is the originalscore of document d with respect to query q, λ is used to scale thetemporal score adjustment, and γεdenotes the set of years that occur indocument d. Documents are then reordered according to S′(q, d) toproduce the final temporally-biased ranking.

The reordering approach described above does not modify the originalquery. Instead, it modifies the scores returned by the underlyingranking function. Therefore, according to another approach, and asdescribed above, temporal references are added to queries. That is,given an implicitly year qualified query q, z(q, y) is computed forevery year associated with q. All years with negligible z(q, y) valuesare then discarded (we use 0.001 as a cutoff). An expanded query is thenconstructed that has the following form:

#query(q#wsyn(≈(q, y ₁)y ₁ . . . z(q,y _(N))y _(N)))

where q is the original query, z(q, y) are the weights as describedabove, and #wsyn is a weighted synonym query operator. The #wsynoperator treats all of the years y1 . . . yN as a single “term” for thepurposes of computing term statistics, such as tf, idf, etc. However,whenever a year within the operator is matched in the document, thematch is weighted according to the associated z(q, y) value. Forexample, the query “SIGIR” (Special Interest Group on InformationRetrieval) is expanded to the following query:

#query(SIGIR#wsyn(0.003 2005 04100 2006 2007 0.706 2008))

For this query, documents that match 2008 will use a text match weightof 0.706 for ranking, whereas documents that match 2005 will use a textmatch weight of just 0.003 for ranking. Given a reasonable rankingfunction that knows how to interpret the weighted synonym queryoperator, documents that match more highly weighted terms, especially inimportant fields such as the anchor text or title, will be rankedhigher, as will documents that match “SIGIR 2008” as an exact phrase.

Embodiments of the present invention may be employed to provide searchservices in any of a wide variety of computing contexts and using any ofa wide variety of technologies. For example, as illustrated in FIG. 4,implementations are contemplated in which the relevant population ofusers interacts with a diverse network environment via any type ofcomputer (e.g., desktop, laptop, tablet, etc.) 402, media computingplatforms 403 (e.g., cable and satellite set top boxes and digital videorecorders), handheld computing devices (e.g., PDAs) 404, cell phones406, or any other type of computing or communication platform. Theprocessing of query logs and the providing of search services inaccordance with the invention are represented in FIG. 4 by server 408and data store 410 which, as will be understood, may correspond tomultiple distributed devices and data stores operated by one or moreentities.

The invention may also be practiced in a wide variety of networkenvironments (represented by network 412) including, for example,TCP/IP-based networks, telecommunications networks, wireless networks,etc. In addition, the computer program instructions with whichembodiments of the invention are implemented may be stored in any typeof tangible computer-readable media, and may be executed according to avariety of computing models including a client/server model, apeer-to-peer model, on a stand-alone computing device, or according to adistributed computing model in which various of the functionalitiesdescribed herein may be effected or employed at different locations.

While the invention has been particularly shown and described withreference to specific embodiments thereof, it will be understood bythose skilled in the art that changes in the form and details of thedisclosed embodiments may be made without departing from the spirit orscope of the invention. For example, embodiments of the presentinvention have been described herein with reference to the generation oforganic search results responsive to a query identified as likely to bean implicitly year qualified query. However, it will be understood thatthe principles described herein may just as readily be applied to thegeneration of sponsored search results. That is, queries may beidentified and expanded in accordance with the invention to generateorganic search results, sponsored search results, or both.

Similarly, embodiments of the invention have been described herein withreference to identifying and expanding queries that are likely to havean implied year. However, it should be understood that embodiments ofthe present invention are not so limited. Rather, the scope of theinvention includes any temporal aspect or reference which may beidentified using the techniques described herein. Such temporalreferences might include, for example, times of day, individual dates ordays of the week, or specific weeks, months, decades, centuries,millennia, etc.

In addition, embodiments of the invention are contemplated in which theability to identify implicitly temporally qualified queries may beleveraged in ways which do not necessarily require reformulation of suchqueries. For example, a “post-processing” approach to document scoringis described above in which a document score based on a conventionalranking function (i.e., S(q, d)) is adjusted (i.e., to get S′(q, d))with reference to mined query log data. However, another approach isalso contemplated in which, instead of modifying a document scoregenerated by a conventionally derived ranking function, a specializedranking function is provided that takes into account the temporalinformation mined from query logs. According to a particular class ofembodiments, such a specialized ranking function is created by trainingthe new function, call it T(q, d), on a more constrained query set,e.g., queries which are explicitly year qualified queries as determined,for example, as described above.

A conventionally derived ranking function depends on many variables(often hundreds). The importance of the variables is tuned to optimizerelevance on a general (i.e., random) query set. However, some featuresthat are useful for temporally qualified queries may not be consideredas important in a general ranking function because there is not enoughevidence in the random training data. By contrast, when temporallyqualified queries are separated out and relevance is optimized withreference to this reduced set of queries, the importance of suchfeatures in the resulting specialized ranking function will beemphasized.

The manner in which such a specialized ranking function could be usedmay be understood with reference to the flowchart of FIG. 5. When aquery is received (502), it is determined whether the query contains atemporal reference, e.g., a reference to a year (504), and if not,whether the query contains a query term represented in the query logdata (506). If a query term is present which is also in the query logdata, the query is considered an implicitly year qualified query, andsearch results are generated and ranked in accordance with thespecialized ranking function, T(q, d) (508). Otherwise, search resultsare generated and ranked using a conventionally derived rankingfunction, S(q, d) (510). It should be noted that such a specializedranking function may be used in combination with the query reformulationdescribed above.

Finally, although various advantages, aspects, and objects of thepresent invention have been discussed herein with reference to variousembodiments, it will be understood that the scope of the inventionshould not be limited by reference to such advantages, aspects, andobjects. Rather, the scope of the invention should be determined withreference to the appended claims.

1. A computer-implemented method for responding to a first search querythat does not include an explicit temporal reference, comprising:receiving the first search query from a user device; determining thatthe first search query is implicitly temporally qualified with referenceto a first query term included in the first search query and query logdata that relate a plurality of previous query terms included inprevious search queries to specific temporal references included in theprevious search queries; expanding the first search query to include oneor more of the specific temporal references; generating first searchresults using the expanded first search query; and transmitting thefirst search results to the user device.
 2. The method of claim 1wherein the query log data represent frequencies with which the previousquery terms were associated with particular ones of the specifictemporal references in the previous search queries.
 3. The method ofclaim 2 further comprising including each of the previous query terms inthe query log data only where the frequency with which each of theprevious query terms was associated with at least one of the specifictemporal references in the previous search queries exceeds aprogrammable threshold.
 4. The method of claim 2 further comprisingincluding each of the previous query terms in the query log data onlywhere the previous queries including the previous query terms had beenreformulated from original queries including the previous query termsbut not explicitly including any of the specific temporal references. 5.The method of claim 1 wherein the first query term is associated with afirst plurality of the specific temporal references in the query logdata, and wherein expanding the first search query comprises expandingthe first search query to include each of the first plurality ofspecific temporal references.
 6. The method of claim 5 wherein the firstsearch results are biased in accordance with a frequency with which eachof the first plurality of specific temporal references is associatedwith the first query term in the query log data.
 7. The method of claim1 wherein the first search results comprise one or more of organicsearch results or sponsored search results.
 8. A computer-implementedmethod for presenting search results on a user device in response to afirst search query that does not include an explicit temporal reference,comprising: providing a search interface on the user device in which auser enters the first search query; and providing a search resultsinterface on the user device in which search results are presented thatare responsive to a first expanded version of the first search query,the first expanded version of the first search query including one ormore temporal reference not included in the first search query.
 9. Themethod of claim 8 further comprising providing an indication in thesearch results interface that the search results are responsive to thefirst expanded version of the first search query rather than the firstsearch query.
 10. The method of claim 9 further comprising providing amechanism in the search results interface by which the user requestsfurther search results responsive to the first search query.
 11. Themethod of claim 9 further comprising providing a mechanism in the searchresults interface by which the user requests further search resultsresponsive to a second expanded version of the first search queryincluding a different temporal reference than the first expanded versionof the first search query.
 12. The method of claim 8 wherein the one ormore temporal references comprises a plurality of temporal references,and wherein the search results are biased in accordance with weightsassociated with each of the temporal references.
 13. The method of claim8 wherein the search results comprise one or more of organic searchresults or sponsored search results.
 14. A computer-implemented methodfor responding to a first search query that does not include an explicittemporal reference, comprising: receiving the first search query from auser device; determining that the first search query is implicitlytemporally qualified with reference to a first query term included inthe first search query and query log data that relate a plurality ofprevious query terms included in previous search queries to specifictemporal references included in the previous search queries; generatingfirst search results responsive to the first search query, the firstsearch results being ranked in accordance with a predetermined rankingfunction derived with reference to the query log data; and transmittingthe first search results to the user device.
 15. The method of claim 14wherein the query log data represent frequencies with which the previousquery terms were associated with particular ones of the specifictemporal references in the previous search queries.
 16. The method ofclaim 15 further comprising including each of the previous query termsin the query log data only where the frequency with which each of theprevious query terms was associated with at least one of the specifictemporal references in the previous search queries exceeds aprogrammable threshold.
 17. The method of claim 15 further comprisingincluding each of the previous query terms in the query log data onlywhere the previous queries including the previous query terms had beenreformulated from original queries including the previous query termsbut not explicitly including any of the specific temporal references.18. The method of claim 14 wherein the first search results comprise oneor more of organic search results or sponsored search results.
 19. Themethod of claim 14 further comprising deriving the predetermined rankingfunction by training the predetermined ranking function on a temporallyqualified query set represented by the query log data.
 20. A system forresponding to a first search query that does not include an explicittemporal reference, the system comprising: at least one data storehaving query log data stored therein, the query log data relating aplurality of previous query terms included in previous search queries tospecific temporal references included in the previous search queries;and at least one computing device configured to: receive the firstsearch query from a user device; determine that the first search queryis implicitly temporally qualified with reference to a first query termincluded in the first search query and the query log data; generatefirst search results responsive to the first search query and taking thequery log data into account; and transmit the first search results tothe user device.